Prediction of glioblastoma cellular infiltration and recurrence using machine learning and multi-parametric MRI analysis: Results from the multi-institutional respond consortium
Recommended Citation
Akbari H, Mohan S, Garcia JA, Fathi Kazerooni A, Sako C, Bakas S, Shukla G, Bagley SJ, Ahn S, Ak M, Alexander GS, Ali AS, Baid U, Bavde C, Brem S, Capellades J, Chang J, Choi Y, Dicker AP, Fathallah-Shaykh H, Flanders AE, Griffith BD, LaMontagne P, Lee M, Lee S, Liem S, Lombardo J, Mahajan A, Milchenko M, Nazeri A, Puig J, Sloan A, Taylor W. Prediction of glioblastoma cellular infiltration and recurrence using machine learning and multi-parametric MRI analysis: Results from the multi-institutional respond consortium. Neuro Oncol 2021; 23(SUPPL 6):vi132-vi133.
Document Type
Conference Proceeding
Publication Date
11-1-2021
Publication Title
Neuro Oncol
Abstract
PURPOSE: Multi-parametric MRI and artificial intelligence (AI) methods were previously used to predict peritumoral neoplastic cell infiltration and risk of future recurrence in glioblastoma, in single-institution studies. We hypothesize that important characteristics of peritumoral tissue heterogeneity captured, engineered/selected, and quantified by these methods relate to predictions generalizable in the multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium. METHODS: To support further development, generalization, and clinical translation of our proposed method, we trained the AI model on a retrospective cohort of 29 de novo glioblastoma patients from the Hospital of the University of Pennsylvania (UPenn) (Male/Female:20/9, age:22-78 years) followed by evaluation on a prospective multi-institutional cohort of 84 glioblastoma patients (Male/Female:51/33, age:34-89 years) from Case Western Reserve University/ University Hospitals (CWRU/UH, 25), New York University (NYU, 13), Ohio State University (OSU, 13), University Hospital Río Hortega (RH, 2), and UPenn (31). Features extracted from pre-resection MRI (T1, T1-Gd, T2, T2-FLAIR, ADC) were used to build our model predicting the spatial pattern of subsequent tumor recurrence. These predictions were evaluated against regions of pathology-confirmed post-resection recurrence. RESULTS: Our model predicted the locations that later harbored tumor recurrence with sensitivity 83%, AUC 0.83 (99% CI, 0.73-0.93), and odds ratio 7.23 (99% CI, 7.09-7.37) in the prospective cohort. Odds ratio (99% CI)/ AUC(99% CI) per institute were: CWRU/UH, 7.8(7.6-8.1)/0.82(0.75-0.89); NYU, 3.5(3.3-3.6)/0.84(074-0.93); OSU, 7.9(7.6-8.3)/0.8(0.67-0.94); RH, 22.7(20-25.1)/0.94(0.27-1); UPenn, 7.1(6.8-7.3)/0.83(0.76-0.91). CONCLUSION: This is the first study that provides relatively extensive multiinstitutional validated evidence that AI can provide good predictions of peritumoral neoplastic cell infiltration and future recurrence, by dissecting the MRI signal heterogeneity in peritumoral tissue. Our analyses leveraged the unique dataset of the ReSPOND consortium, which aims to develop and evaluate AI-based biomarkers for individualized prediction and prognostication, by moving from single-institution studies to generalizable, well validated multi-institutional predictive biomarkers.
Volume
23
Issue
SUPPL 6
First Page
vi132-vi133
